Research on Image Acquisition Preprocessing and Data Augmentation Algorithm Based on Strip Surface Defect Detection Technology

Abstract

The identification of strip steel surface defect is an important index to test its quality. With the development of deep learning technology, strip surface detection has developed from traditional machine learning algorithm recognition to deep neural network detection. Existing deep learning strip detection algorithms mainly focus on the tuning and identification of standard defect data sets, while few focus on the preliminary preparation processes such as the collection and pretreatment of the original data of the production line. To address this problem, this paper proposes a series of image data acquisition and pre-processing algorithms for strip surface defect detection technology, such as out-of-area background shielding, noise filtering, suspected defect image acquisition, light equalization and image enhancement processing. Light equalization and image enhancement processing and a series of image data acquisition and pre-processing algorithms to quickly eliminate invalid data from the collected image data and retain valid data. And for the image ROI region data broadening, effectively solve the problem of insufficient defect samples, for the existing deep learning strip inspection algorithm to provide a standard training and testing data set. The image acquisition pre-processing and data augmentation algorithm in this paper is also of practical value when extended to other products in the field of surface inspection.